The insider threat within organisational cybersecurity continues to be of great concern globally. The current insider threat detection strategies are acknowledged as ineffective, evidenced by the increased reported events in high-profile insider threats and cyber data loss cases borne from insider and privilege misuse. The impact of insider incidents on Financial Service (FS) organisations is vast, operationally disruptive, and costly from a regulatory, financial, and reputational perspective. Many United Kingdom (UK) FS organisations have invested in insider risk programmes, but there is no sign of the insider threat diminishing. This paper will address the following research questions: (1) What factors influence employees to become malicious insider threats and apply this to employees working within the UK? (2) What preventative measures could be effectively operationalised within UK FS organisations to prevent malicious insider attacks? A literature review was conducted, reviewing 54 articles in peer-reviewed journals. Additional and relevant articles were incorporated to enrich the review, further substantiating the academic currency and context of the study. The review reveals five primary emerging insider threat themes, subsequently discussed and including behavioural indicators, information security behaviours, technical controls, insider threat strategies, and regulation. Throughout the literature review, one primary challenge highlighted the lack of articles published concerning the FS industry; however, the studies reviewed were relevant, appropriate, and applied across this review. Furthermore, the review also considers outcomes from a practitioner’s perspective, offering insights into the limitations of insider threat approaches and strategies and offering potential recommendations.

Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or data poisoning attempts where threat actors can inject malicious data into the system. There is a need for a detection system that mitigates malicious sensor data to maintain the integrity and reliability of the collected information. This paper addresses this issue by proposing an adaptive and robust model for detecting malicious data in MCS scenarios involving sensor data from mobile devices. The proposed model incorporates an adaptive learning mechanism that enables the TCN-based model to continually evolve and adapt to new patterns, enhancing its capability to detect novel malicious data as threats evolve. We also present a comprehensive evaluation of the proposed model’s performance using the SherLock datasets, demonstrating its effectiveness in accurately detecting malicious sensor data and mitigating potential threats to the integrity of MCS systems. Comparative analysis with existing models highlights the performance of the proposed TCN-based model in terms of detection accuracy, with an accuracy score of 98%. Through these contributions, the paper aims to advance the state of the art in ensuring the trustworthiness and security of MCS systems, paving the way for the development of more reliable and robust crowdsensing applications.